Capital is Back: From Labor to Capital in the Modern Economy

economy
python
Wealth-Income Ratios in Advanced Economies 1980-2023
Published
Aug 14, 2025
Keywords
wealth-income
Summary

The chart illustration the evolution of thel wealth-income ratio from 1980 to 2023 highlights the interplay between wealth accumulation and income generation over last five decades. It reveals a clear upward trend, reflecting the disproportionate growth of wealth relative to income, particularly in recent decades. This relationship is largely determined by the growth of the economy relative to the growth of capital. When capital grows at a faster rate than the economy, wealth concentrates disproportionately, amplifying disparities and altering the balance of economic power.
Code
# Libraries
# ===================================================
import requests
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patheffects as patheffects
import matplotlib.font_manager as fm
import matplotlib.image as mpimg
from io import BytesIO

# Extract Data (Countries)
# ===================================================
# Extract JSON to dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3'})

# Extract Data (WID)
# ===================================================
# Extract PARQUET to dataframe
url = "https://raw.githubusercontent.com/guillemmaya92/Analytics/master/Data/WID_Values.parquet"
df = pd.read_parquet(url, engine="pyarrow")

# Transform Data
# ===================================================
# Filter nulls and countries
df = df[df['wiratio'].notna()]
df = pd.merge(df, df_countries, left_on='country', right_on='ISO2', how='inner')

# Rename columns
df = df.rename(
        columns={
            'Country_Abr': 'country_name',
            'wiratio': 'beta'
        }
    )

# Filter countries have data post 1980
dfx = df.loc[df['year'] == 1980, 'country']
df = df[df['country'].isin(dfx)]
df = df[df['year'] >= 1980]
df = df[df['Analytical'] == 'Advanced Economies']

# Dataframe countries
dfc = df[df['country'].isin(['CN', 'US', 'FR', 'DE', 'ES'])]

# Select columns and order
df = df[['year', 'country', 'country_name', 'beta']]

print(df)

# Visualization Data
# ===================================================
# Font Style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 10})

# Create color dictionaire 
palette = {'CN': '#ffc2c2', 'US': '#c2d2ff', 'FR': '#c2ffcb', 'DE': '#e5c2ff', 'ES': '#fffac2'}

# Create line plots
plt.figure(figsize=(12, 8))
sns.lineplot(data=df, x='year', y='beta', hue='country', linewidth=0.3, alpha=0.5, palette=['gray'], legend=False)
sns.lineplot(data=dfc, x='year', y='beta', hue='country', linewidth=2.25, palette=['black'], legend=False)
sns.lineplot(data=dfc, x='year', y='beta', hue='country', linewidth=1.5, palette=palette, legend=False)

# Custom plot
plt.text(0, 1.08, 'Capital is back', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0, 1.045, 'Wealth-Income Ratios in Advanced Economies 1980-2023', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.xlabel('Year', fontsize=10, fontweight='bold')
plt.ylabel('Wealth-Income Ratio', fontsize=10, fontweight='bold')
plt.grid(axis='x', alpha=0.7, linestyle=':')
plt.ylim(0, 12)
plt.xlim(1980, 2026)
plt.xticks(range(1980, 2026, 10))
plt.tight_layout()

# Delete spines
for spine in ["top", "right"]:
    plt.gca().spines[spine].set_visible(False)

# Add Data Source
plt.text(0, -0.08, 'Data Source: World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')

# Add Notes
plt.text(0, -0.1, 'Notes: Wealth-Income Ratio is the division of national wealth by national income.', 
    transform=plt.gca().transAxes, 
    fontsize=7,
    fontstyle='italic',
    color='gray')

 # Add Year label
formatted_date = 2023
plt.text(1, 1.08, f'{formatted_date}',
    transform=plt.gca().transAxes,
    fontsize=20, ha='right', va='top',
    fontweight='bold', color='#D3D3D3')

# Legend values
beta_cn = round(df[(df['country'] == 'CN') & (df['year'] == 2023)]['beta'].values[0], 1)
beta_de = round(df[(df['country'] == 'DE') & (df['year'] == 2023)]['beta'].values[0], 1)
beta_es = round(df[(df['country'] == 'ES') & (df['year'] == 2023)]['beta'].values[0], 1)
beta_fr = round(df[(df['country'] == 'FR') & (df['year'] == 2023)]['beta'].values[0], 1)
beta_us = round(df[(df['country'] == 'US') & (df['year'] == 2023)]['beta'].values[0], 1)

# Legend lines
line1 = mlines.Line2D([], [], color=palette['CN'], label=f'China: {beta_cn}', linewidth=2)
line2 = mlines.Line2D([], [], color=palette['DE'], label=f'Germany: {beta_de}', linewidth=2)
line3 = mlines.Line2D([], [], color=palette['ES'], label=f'Spain: {beta_es}', linewidth=2)
line4 = mlines.Line2D([], [], color=palette['FR'], label=f'France: {beta_fr}', linewidth=2)
line5 = mlines.Line2D([], [], color=palette['US'], label=f'USA: {beta_us}', linewidth=2)
line6 = mlines.Line2D([], [], color='grey', label=f'Advanced economies', linewidth=1)
line1.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])
line2.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])
line3.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])
line4.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])
line5.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])

# Legend plot
plt.legend(handles=[line1, line2, line3, line4, line5, line6], title='Countries', fontsize=9, title_fontproperties=fm.FontProperties(weight='bold'))

# Define flags
flag_urls = {
    'CN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/CN.png',
    'US': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/US.png',
    'FR': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/FR.png',
    'ES': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/ES.png',
    'DE': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/DE.png'
}

# Load flags
flags = {country: mpimg.imread(BytesIO(requests.get(url).content)) for country, url in flag_urls.items()}

# Add flags
year = 2023
# Adjust flags items
for country, flag in flags.items():
    # Find beta for each country
    beta_value = df[(df['country'] == country) & (df['year'] == year)]['beta'].values[0]
    
    if country == 'CN':
        plt.imshow(flag, aspect='auto', extent=[year+1, year+2, beta_value - 0.2, beta_value + 0.2], alpha=0.7)
    elif country == 'DE':
        plt.imshow(flag, aspect='auto', extent=[year+1, year+2, beta_value - 0.2, beta_value + 0.2], alpha=0.7)
    elif country == 'ES':
        plt.imshow(flag, aspect='auto', extent=[year+1, year+2, beta_value - 0.2, beta_value + 0.2], alpha=0.7)
    elif country == 'FR':
        plt.imshow(flag, aspect='auto', extent=[year+1, year+2, beta_value - 0.4, beta_value + 0], alpha=0.7)
    elif country == 'US':
        plt.imshow(flag, aspect='auto', extent=[year+1, year+2, beta_value - 0.4, beta_value + 0], alpha=0.7)

# Save the animation :)
plt.savefig("C:/Users/guill/Downloads/FIG_WID_Beta_Evolution.png", dpi=300, bbox_inches='tight') 

# Show plot
plt.show()